Boutique .NET Consulting for Healthcare & Legal Technology | Minneapolis

Q: What does Parka Software do?

Parka Software is a boutique .NET consultancy in Minneapolis working with healthcare organizations and law firms. The work covers system integrations, legacy .NET migrations, custom application development and rescuing software projects that have stalled or lost the developer who understood them.

Q: Who actually does the work on an engagement?

The engineer who scopes the project is the engineer who builds it. There are no account managers, no handoffs after the contract is signed and no junior developers substituted in later. Where a project needs additional capacity, it comes from a known network of engineers and the client is told who is on the project and what they are responsible for.

Q: How does an engagement start?

Engagements start with a no-cost conversation about the system and the problem, followed where appropriate by a fixed-fee assessment of the code, data, integrations and deployment. The assessment produces a written account of what exists, the real risks and what the options cost, and it stands on its own — clients can take it to their internal team.

Q: Does AI replace the need for a .NET consultant?

AI has made generating code substantially cheaper, and AI tooling is used heavily in the work, which compresses timelines and lowers cost. It has not replaced domain judgment: knowing the regulatory display rules for clinical outcome data, knowing which integrations a document management upgrade will break, and knowing what a specific client environment will support. A growing category of work is rescuing systems that were built quickly with AI assistance and are now in production without anyone on staff able to safely modify them.

Q: What industries does Parka Software specialize in?

Healthcare organizations running data registries and regulatory reporting platforms, and law firms running document management, billing and research systems. Past platforms include work for the Scientific Registry of Transplant Recipients, HRSA, the United States Renal Data System at NIH/NIDDK, the Minnesota Secretary of State and AmLaw 200 firms.

Selected work

Platforms built for the Scientific Registry of Transplant Recipients and HRSA, the United States Renal Data System at NIH/NIDDK, the Minnesota Secretary of State, AGC of Minnesota and AmLaw 200 law firms.

See the work

These situations are common.

An application running a core business process was built years ago and has never been replaced — it still works, and the cost and risk of replacing it are real. The people who built it have moved on. The institutional knowledge about how it operates has largely gone with them.

Two systems that need to share data don't. The gap has been filled with manual steps and workarounds that have accumulated their own complexity. Getting the integration done properly has stayed on the roadmap, but scoping what it would actually take keeps getting deferred.

An outside firm is on the project. The engineer who did the initial scoping has moved to other engagements. Six months in, the people doing the work are building from documentation rather than from the original decisions — and some of what drove those decisions didn't make it into the docs.

The document management platform is the system of record for everything the organization runs. The integrations connecting it to billing, search and the client portal have been built up over years. What they do, and what a platform upgrade would affect, is knowledge that lives with a handful of people who may not always be around.

Something was built quickly — by a contractor, by an internal team under deadline, or with heavy AI assistance — and it runs. Now it needs to change, and nobody can say with confidence what will break when it does.

A small firm on purpose.

Most consultancies solve the capacity problem by adding people. That works for staffing. It works badly for judgment, because the person who understood your system in the sales meeting is rarely the person maintaining it in month four.

Parka Software is built the other way around. Engagements are deliberately limited so that the engineer who scopes the work is the engineer who executes it. Where a project needs more capacity, it comes from a network of engineers who have been worked with directly — and you know who they are and what they own before they start.

The practical difference shows up when something goes wrong. There is no account manager to route the question through, no discovery phase to re-explain your environment to, and no handoff document standing in for the reasoning behind a decision.

More about the practice

How engagements run

Four stages. The first one is short and tells you whether the rest is worth doing.

01 — Conversation

You describe the system and what's wrong with it. If it's something that's been handled before, you'll hear that, along with where the hard parts usually are. If it isn't a fit, you'll hear that instead. No cost, no pitch deck.

02 — Assessment

A fixed-fee review of the actual system: the code, the data, the integrations and the deployment. It produces a written account of what exists, what the real risks are, and what the options cost. It stands on its own — plenty of clients take the assessment and hand it to their internal team.

03 — Build

Fixed scope, working software in front of you throughout, and direct access to the engineer building it. Scope changes are priced when they come up rather than absorbed quietly and surfaced as a delay later.

04 — Handoff

Documentation aimed at the team that inherits the system, covering why decisions were made and not just what was built. The goal is that your team can maintain it without calling. Ongoing support is available; dependency is not the business model.

On AI

A fair question in 2026 is why you'd hire anyone to write software when your team can generate a great deal of it directly.

The honest answer is that generating code stopped being the expensive part. AI tooling is used heavily here, and it has genuinely compressed timelines — work that used to take weeks often takes days, and that shows up in what an engagement costs you.

What it hasn't changed is the part that actually determines whether a project succeeds. Knowing that a low-volume transplant center's outcomes have to be suppressed rather than charted alongside a high-volume center's. Knowing which three billing integrations a document management upgrade will quietly break. Knowing that the constraint on this project is a security review board, not a sprint. That knowledge comes from having been in these systems before, and a model that has never seen your environment can't supply it.

The practical result is that AI has made this kind of firm more viable, not less. A two-person effort now delivers what used to require a team, at a price a larger agency's overhead can't match. It has also created a steady stream of a new kind of work: systems built fast with AI assistance, now in production, that nobody on staff can safely modify.

Start with the project.

Legacy system, stalled integration, greenfield application, project that needs rescuing. Describe what you're dealing with and we can figure out what the work actually involves.

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